From dc2d82f685b7d296edc3b8d21e11739fe7d0b9c0 Mon Sep 17 00:00:00 2001 From: sam Date: Mon, 6 Oct 2025 15:46:51 +0800 Subject: [PATCH] update --- app/ui/views/backtest.py | 677 +++++++-------------------------------- 1 file changed, 122 insertions(+), 555 deletions(-) diff --git a/app/ui/views/backtest.py b/app/ui/views/backtest.py index 8641f16..afb8a2f 100644 --- a/app/ui/views/backtest.py +++ b/app/ui/views/backtest.py @@ -370,135 +370,137 @@ def render_backtest_review() -> None: action_values.append(action_val) controls_valid = True - with st.expander("部门 LLM 参数", expanded=False): - dept_codes = sorted(app_cfg.departments.keys()) - if not dept_codes: - st.caption("当前未配置部门。") - else: - selected_departments = st.multiselect( - "选择需要调整的部门", - dept_codes, - default=[], - key="decision_env_departments", + + st.divider() + st.subheader("部门参数调整（可选）") + dept_codes = sorted(app_cfg.departments.keys()) + if not dept_codes: + st.caption("当前未配置部门。") + else: + selected_departments = st.multiselect( + "选择需要调整的部门", + dept_codes, + default=[], + key="decision_env_departments", + ) + tool_policy_values = ["auto", "none", "required"] + for dept_code in selected_departments: + settings = app_cfg.departments.get(dept_code) + if not settings: + continue + st.subheader(f"部门：{settings.title or dept_code}") + base_temp = 0.2 + if settings.llm and settings.llm.primary and settings.llm.primary.temperature is not None: + base_temp = float(settings.llm.primary.temperature) + prefix = f"decision_env_dept_{dept_code}" + col_tmin, col_tmax, col_tslider = st.columns([1, 1, 2]) + temp_min = col_tmin.number_input( + "温度最小值", + min_value=0.0, + max_value=2.0, + value=max(0.0, base_temp - 0.3), + step=0.05, + key=f"{prefix}_temp_min", ) - tool_policy_values = ["auto", "none", "required"] - for dept_code in selected_departments: - settings = app_cfg.departments.get(dept_code) - if not settings: - continue - st.subheader(f"部门：{settings.title or dept_code}") - base_temp = 0.2 - if settings.llm and settings.llm.primary and settings.llm.primary.temperature is not None: - base_temp = float(settings.llm.primary.temperature) - prefix = f"decision_env_dept_{dept_code}" - col_tmin, col_tmax, col_tslider = st.columns([1, 1, 2]) - temp_min = col_tmin.number_input( - "温度最小值", - min_value=0.0, - max_value=2.0, - value=max(0.0, base_temp - 0.3), - step=0.05, - key=f"{prefix}_temp_min", + temp_max = col_tmax.number_input( + "温度最大值", + min_value=0.0, + max_value=2.0, + value=min(2.0, base_temp + 0.3), + step=0.05, + key=f"{prefix}_temp_max", + ) + if temp_max <= temp_min: + controls_valid = False + st.warning("温度最大值必须大于最小值。") + temp_max = min(2.0, temp_min + 0.01) + span = temp_max - temp_min + if span <= 0: + ratio_default = 0.0 + else: + clamped = min(max(base_temp, temp_min), temp_max) + ratio_default = (clamped - temp_min) / span + temp_action = col_tslider.slider( + "动作值（映射至温度区间）", + min_value=0.0, + max_value=1.0, + value=float(ratio_default), + step=0.01, + key=f"{prefix}_temp_action", + ) + specs.append( + ParameterSpec( + name=f"dept_temperature_{dept_code}", + target=f"department.{dept_code}.temperature", + minimum=temp_min, + maximum=temp_max, ) - temp_max = col_tmax.number_input( - "温度最大值", - min_value=0.0, - max_value=2.0, - value=min(2.0, base_temp + 0.3), - step=0.05, - key=f"{prefix}_temp_max", + ) + spec_labels.append(f"department:{dept_code}:temperature") + action_values.append(temp_action) + + col_tool, col_hint = st.columns([1, 2]) + tool_choice = col_tool.selectbox( + "函数调用策略", + tool_policy_values, + index=tool_policy_values.index("auto"), + key=f"{prefix}_tool_choice", + ) + col_hint.caption("映射提示：0→auto，0.5→none，1→required。") + if len(tool_policy_values) > 1: + tool_value = tool_policy_values.index(tool_choice) / (len(tool_policy_values) - 1) + else: + tool_value = 0.0 + specs.append( + ParameterSpec( + name=f"dept_tool_{dept_code}", + target=f"department.{dept_code}.function_policy", + values=tool_policy_values, ) - if temp_max <= temp_min: - controls_valid = False - st.warning("温度最大值必须大于最小值。") - temp_max = min(2.0, temp_min + 0.01) - span = temp_max - temp_min - if span <= 0: - ratio_default = 0.0 - else: - clamped = min(max(base_temp, temp_min), temp_max) - ratio_default = (clamped - temp_min) / span - temp_action = col_tslider.slider( - "动作值（映射至温度区间）", - min_value=0.0, - max_value=1.0, - value=float(ratio_default), - step=0.01, - key=f"{prefix}_temp_action", + ) + spec_labels.append(f"department:{dept_code}:tool_choice") + action_values.append(tool_value) + + template_id = (settings.prompt_template_id or f"{dept_code}_dept").strip() + versions = [ver for ver in TemplateRegistry.list_versions(template_id) if isinstance(ver, str)] + if versions: + active_version = TemplateRegistry.get_active_version(template_id) + default_version = ( + settings.prompt_template_version + or active_version + or versions[0] + ) + try: + default_index = versions.index(default_version) + except ValueError: + default_index = 0 + version_choice = st.selectbox( + "提示模板版本", + versions, + index=default_index, + key=f"{prefix}_template_version", + help="离散动作将按版本列表顺序映射，可用于强化学习优化。", + ) + selected_index = versions.index(version_choice) + ratio = ( + 0.0 + if len(versions) == 1 + else selected_index / (len(versions) - 1) ) specs.append( ParameterSpec( - name=f"dept_temperature_{dept_code}", - target=f"department.{dept_code}.temperature", - minimum=temp_min, - maximum=temp_max, + name=f"dept_prompt_version_{dept_code}", + target=f"department.{dept_code}.prompt_template_version", + values=list(versions), ) ) - spec_labels.append(f"department:{dept_code}:temperature") - action_values.append(temp_action) - - col_tool, col_hint = st.columns([1, 2]) - tool_choice = col_tool.selectbox( - "函数调用策略", - tool_policy_values, - index=tool_policy_values.index("auto"), - key=f"{prefix}_tool_choice", + spec_labels.append(f"department:{dept_code}:prompt_version") + action_values.append(ratio) + st.caption( + f"激活版本：{active_version or '默认'} ｜ 当前选择：{version_choice}" ) - col_hint.caption("映射提示：0→auto，0.5→none，1→required。") - if len(tool_policy_values) > 1: - tool_value = tool_policy_values.index(tool_choice) / (len(tool_policy_values) - 1) - else: - tool_value = 0.0 - specs.append( - ParameterSpec( - name=f"dept_tool_{dept_code}", - target=f"department.{dept_code}.function_policy", - values=tool_policy_values, - ) - ) - spec_labels.append(f"department:{dept_code}:tool_choice") - action_values.append(tool_value) - - template_id = (settings.prompt_template_id or f"{dept_code}_dept").strip() - versions = [ver for ver in TemplateRegistry.list_versions(template_id) if isinstance(ver, str)] - if versions: - active_version = TemplateRegistry.get_active_version(template_id) - default_version = ( - settings.prompt_template_version - or active_version - or versions[0] - ) - try: - default_index = versions.index(default_version) - except ValueError: - default_index = 0 - version_choice = st.selectbox( - "提示模板版本", - versions, - index=default_index, - key=f"{prefix}_template_version", - help="离散动作将按版本列表顺序映射，可用于强化学习优化。", - ) - selected_index = versions.index(version_choice) - ratio = ( - 0.0 - if len(versions) == 1 - else selected_index / (len(versions) - 1) - ) - specs.append( - ParameterSpec( - name=f"dept_prompt_version_{dept_code}", - target=f"department.{dept_code}.prompt_template_version", - values=list(versions), - ) - ) - spec_labels.append(f"department:{dept_code}:prompt_version") - action_values.append(ratio) - st.caption( - f"激活版本：{active_version or '默认'} ｜ 当前选择：{version_choice}" - ) - else: - st.caption("当前模板未注册可选提示词版本，继续沿用激活版本。") + else: + st.caption("当前模板未注册可选提示词版本，继续沿用激活版本。") if specs: st.caption("动作维度顺序：" + "，".join(spec_labels)) @@ -509,213 +511,6 @@ def render_backtest_review() -> None: help="关闭部门调用后不依赖外部 LLM 网络，仅根据规则代理权重模拟。", ) - st.divider() - st.subheader("单次调参") - - run_decision_env = st.button("执行单次调参", key="run_decision_env_button") - just_finished_single = False - if run_decision_env: - if not specs: - st.warning("请至少配置一个动作维度（代理或部门参数）。") - elif selected_agents and not range_valid: - st.error("请确保所有代理的最大权重大于最小权重。") - elif not controls_valid: - st.error("请修正部门参数的取值范围。") - else: - LOGGER.info( - "离线调参（单次）按钮点击，已选择代理=%s 动作=%s disable_departments=%s", - selected_agents, - action_values, - disable_departments, - extra=LOG_EXTRA, - ) - baseline_weights = app_cfg.agent_weights.as_dict() - for agent in agent_objects: - baseline_weights.setdefault(agent.name, 1.0) - - universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] - if not universe_env: - st.error("请先指定至少一个股票代码。") - else: - bt_cfg_env = BtConfig( - id="decision_env_streamlit", - name="DecisionEnv Streamlit", - start_date=start_date, - end_date=end_date, - universe=universe_env, - params={ - "target": target, - "stop": stop, - "hold_days": int(hold_days), - }, - method=app_cfg.decision_method, - ) - env = DecisionEnv( - bt_config=bt_cfg_env, - parameter_specs=specs, - baseline_weights=baseline_weights, - disable_departments=disable_departments, - ) - env.reset() - LOGGER.debug( - "离线调参（单次）启动 DecisionEnv：cfg=%s 参数维度=%s", - bt_cfg_env, - len(specs), - extra=LOG_EXTRA, - ) - with st.spinner("正在执行离线调参……"): - try: - observation, reward, done, info = env.step(action_values) - LOGGER.info( - "离线调参（单次）完成，obs=%s reward=%.4f done=%s", - observation, - reward, - done, - extra=LOG_EXTRA, - ) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("DecisionEnv 调用失败", extra=LOG_EXTRA) - st.error(f"离线调参失败：{exc}") - st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None) - else: - if observation.get("failure"): - st.error("调参失败：回测执行未完成，可能是 LLM 网络不可用或参数异常。") - st.json(observation) - st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None) - else: - resolved_experiment_id = experiment_id or str(uuid.uuid4()) - resolved_strategy = strategy_label or "DecisionEnv" - action_payload = { - label: value for label, value in zip(spec_labels, action_values) - } - metrics_payload = dict(observation) - metrics_payload["reward"] = reward - metrics_payload["department_controls"] = info.get("department_controls") - log_success = False - try: - log_tuning_result( - experiment_id=resolved_experiment_id, - strategy=resolved_strategy, - action=action_payload, - reward=reward, - metrics=metrics_payload, - weights=info.get("weights", {}), - ) - except Exception: # noqa: BLE001 - LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA) - else: - log_success = True - LOGGER.info( - "离线调参（单次）日志写入成功：experiment=%s strategy=%s", - resolved_experiment_id, - resolved_strategy, - extra=LOG_EXTRA, - ) - st.session_state[_DECISION_ENV_SINGLE_RESULT_KEY] = { - "observation": dict(observation), - "reward": float(reward), - "weights": info.get("weights", {}), - "department_controls": info.get("department_controls"), - "actions": action_payload, - "nav_series": info.get("nav_series"), - "trades": info.get("trades"), - "portfolio_snapshots": info.get("portfolio_snapshots"), - "portfolio_trades": info.get("portfolio_trades"), - "risk_breakdown": info.get("risk_breakdown"), - "spec_labels": list(spec_labels), - "action_values": list(action_values), - "experiment_id": resolved_experiment_id, - "strategy_label": resolved_strategy, - "logged": log_success, - } - just_finished_single = True - single_result = st.session_state.get(_DECISION_ENV_SINGLE_RESULT_KEY) - if single_result: - if just_finished_single: - st.success("离线调参完成") - else: - st.success("离线调参结果（最近一次运行）") - st.caption( - f"实验 ID：{single_result.get('experiment_id', '-') } | 策略：{single_result.get('strategy_label', 'DecisionEnv')}" - ) - observation = single_result.get("observation", {}) - reward = float(single_result.get("reward", 0.0)) - col_metrics = st.columns(4) - col_metrics[0].metric("总收益", f"{observation.get('total_return', 0.0):+.2%}") - col_metrics[1].metric("最大回撤", f"{observation.get('max_drawdown', 0.0):+.2%}") - col_metrics[2].metric("波动率", f"{observation.get('volatility', 0.0):+.2%}") - col_metrics[3].metric("奖励", f"{reward:+.4f}") - - turnover_ratio = float(observation.get("turnover", 0.0) or 0.0) - turnover_value = float(observation.get("turnover_value", 0.0) or 0.0) - risk_count = float(observation.get("risk_count", 0.0) or 0.0) - col_metrics_extra = st.columns(3) - col_metrics_extra[0].metric("平均换手率", f"{turnover_ratio:.2%}") - col_metrics_extra[1].metric("成交额", f"{turnover_value:,.0f}") - col_metrics_extra[2].metric("风险事件数", f"{int(risk_count)}") - - weights_dict = single_result.get("weights") or {} - if weights_dict: - st.write("调参后权重：") - st.json(weights_dict) - if st.button("保存这些权重为默认配置", key="save_decision_env_weights_single"): - try: - app_cfg.agent_weights.update_from_dict(weights_dict) - save_config(app_cfg) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("保存权重失败", extra={**LOG_EXTRA, "error": str(exc)}) - st.error(f"写入配置失败：{exc}") - else: - st.success("代理权重已写入 config.json") - - if single_result.get("logged"): - st.caption("调参结果已写入 tuning_results 表。") - - nav_series = single_result.get("nav_series") or [] - if nav_series: - try: - nav_df = pd.DataFrame(nav_series) - if {"trade_date", "nav"}.issubset(nav_df.columns): - nav_df = nav_df.sort_values("trade_date") - nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"]) - st.line_chart(nav_df.set_index("trade_date")["nav"], height=220) - except Exception: # noqa: BLE001 - LOGGER.debug("导航曲线绘制失败", extra=LOG_EXTRA) - - trades = single_result.get("trades") or [] - if trades: - st.write("成交记录：") - st.dataframe(pd.DataFrame(trades), hide_index=True, width='stretch') - - snapshots = single_result.get("portfolio_snapshots") or [] - if snapshots: - with st.expander("投资组合快照", expanded=False): - st.dataframe(pd.DataFrame(snapshots), hide_index=True, width='stretch') - - portfolio_trades = single_result.get("portfolio_trades") or [] - if portfolio_trades: - with st.expander("组合成交明细", expanded=False): - st.dataframe(pd.DataFrame(portfolio_trades), hide_index=True, width='stretch') - - risk_breakdown = single_result.get("risk_breakdown") or {} - if risk_breakdown: - with st.expander("风险事件统计", expanded=False): - st.json(risk_breakdown) - - department_info = single_result.get("department_controls") or {} - if department_info: - with st.expander("部门控制参数", expanded=False): - st.json(department_info) - - action_snapshot = single_result.get("actions") or {} - if action_snapshot: - with st.expander("动作明细", expanded=False): - st.json(action_snapshot) - - if st.button("清除单次调参结果", key="clear_decision_env_single"): - st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None) - st.success("已清除单次调参结果缓存。") - st.divider() st.subheader("自动探索（epsilon-greedy）") col_ep, col_eps, col_seed = st.columns([1, 1, 1]) @@ -880,232 +675,4 @@ def render_backtest_review() -> None: st.session_state.pop(_DECISION_ENV_BANDIT_RESULTS_KEY, None) st.success("已清除自动探索结果。") - st.divider() - st.subheader("批量调参") - st.caption("批量调参：在下方输入多组动作，每行表示一组 0-1 之间的值，用逗号分隔。") - default_grid = "\n".join( - [ - ",".join(["0.2" for _ in specs]), - ",".join(["0.5" for _ in specs]), - ",".join(["0.8" for _ in specs]), - ] - ) if specs else "" - action_grid_raw = st.text_area( - "动作列表", - value=default_grid, - height=120, - key="decision_env_batch_actions", - ) - run_batch = st.button("批量执行调参", key="run_decision_env_batch") - batch_just_ran = False - if run_batch: - if not specs: - st.warning("请至少配置一个动作维度。") - elif selected_agents and not range_valid: - st.error("请确保所有代理的最大权重大于最小权重。") - elif not controls_valid: - st.error("请修正部门参数的取值范围。") - else: - LOGGER.info( - "离线调参（批量）按钮点击，已选择代理=%s disable_departments=%s", - selected_agents, - disable_departments, - extra=LOG_EXTRA, - ) - lines = [line.strip() for line in action_grid_raw.splitlines() if line.strip()] - if not lines: - st.warning("请在文本框中输入至少一组动作。") - else: - LOGGER.debug( - "离线调参（批量）原始输入=%s", - lines, - extra=LOG_EXTRA, - ) - parsed_actions: List[List[float]] = [] - for line in lines: - try: - values = [float(val.strip()) for val in line.split(',') if val.strip()] - except ValueError: - st.error(f"无法解析动作行：{line}") - parsed_actions = [] - break - if len(values) != len(specs): - st.error(f"动作维度不匹配（期望 {len(specs)} 个值）：{line}") - parsed_actions = [] - break - parsed_actions.append(values) - if parsed_actions: - LOGGER.info( - "离线调参（批量）解析动作成功，数量=%s", - len(parsed_actions), - extra=LOG_EXTRA, - ) - baseline_weights = app_cfg.agent_weights.as_dict() - for agent in agent_objects: - baseline_weights.setdefault(agent.name, 1.0) - universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] - if not universe_env: - st.error("请先指定至少一个股票代码。") - else: - bt_cfg_env = BtConfig( - id="decision_env_streamlit_batch", - name="DecisionEnv Batch", - start_date=start_date, - end_date=end_date, - universe=universe_env, - params={ - "target": target, - "stop": stop, - "hold_days": int(hold_days), - }, - method=app_cfg.decision_method, - ) - env = DecisionEnv( - bt_config=bt_cfg_env, - parameter_specs=specs, - baseline_weights=baseline_weights, - disable_departments=disable_departments, - ) - results: List[Dict[str, object]] = [] - resolved_experiment_id = experiment_id or str(uuid.uuid4()) - resolved_strategy = strategy_label or "DecisionEnv" - LOGGER.debug( - "离线调参（批量）启动 DecisionEnv：cfg=%s 动作组=%s", - bt_cfg_env, - len(parsed_actions), - extra=LOG_EXTRA, - ) - with st.spinner("正在批量执行调参……"): - for idx, action_vals in enumerate(parsed_actions, start=1): - env.reset() - try: - observation, reward, done, info = env.step(action_vals) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("批量调参失败", extra=LOG_EXTRA) - results.append( - { - "序号": idx, - "动作": action_vals, - "状态": "error", - "错误": str(exc), - } - ) - continue - if observation.get("failure"): - results.append( - { - "序号": idx, - "动作": action_vals, - "状态": "failure", - "奖励": -1.0, - } - ) - else: - LOGGER.info( - "离线调参（批量）第 %s 组完成，reward=%.4f obs=%s", - idx, - reward, - observation, - extra=LOG_EXTRA, - ) - action_payload = { - label: value - for label, value in zip(spec_labels, action_vals) - } - metrics_payload = dict(observation) - metrics_payload["reward"] = reward - metrics_payload["department_controls"] = info.get("department_controls") - weights_payload = info.get("weights", {}) - try: - log_tuning_result( - experiment_id=resolved_experiment_id, - strategy=resolved_strategy, - action=action_payload, - reward=reward, - metrics=metrics_payload, - weights=weights_payload, - ) - except Exception: # noqa: BLE001 - LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA) - results.append( - { - "序号": idx, - "动作": action_payload, - "状态": "ok", - "总收益": observation.get("total_return", 0.0), - "最大回撤": observation.get("max_drawdown", 0.0), - "波动率": observation.get("volatility", 0.0), - "奖励": reward, - "权重": weights_payload, - "部门控制": info.get("department_controls"), - } - ) - st.session_state[_DECISION_ENV_BATCH_RESULTS_KEY] = { - "results": results, - "selectable": [ - row - for row in results - if row.get("状态") == "ok" and row.get("权重") - ], - "experiment_id": resolved_experiment_id, - "strategy_label": resolved_strategy, - } - batch_just_ran = True - LOGGER.info( - "离线调参（批量）执行结束，总结果条数=%s", - len(results), - extra=LOG_EXTRA, - ) - batch_state = st.session_state.get(_DECISION_ENV_BATCH_RESULTS_KEY) - if batch_state: - results = batch_state.get("results") or [] - if results: - if batch_just_ran: - st.success("批量调参完成") - else: - st.success("批量调参结果（最近一次运行）") - st.caption( - f"实验 ID：{batch_state.get('experiment_id', '-') } | 策略：{batch_state.get('strategy_label', 'DecisionEnv')}" - ) - results_df = pd.DataFrame(results) - st.write("批量调参结果：") - st.dataframe(results_df, hide_index=True, width='stretch') - selectable = batch_state.get("selectable") or [] - if selectable: - option_labels = [ - f"序号 {row['序号']} | 奖励 {row.get('奖励', 0.0):+.4f}" - for row in selectable - ] - selected_label = st.selectbox( - "选择要保存的记录", - option_labels, - key="decision_env_batch_select", - ) - selected_row = None - for label, row in zip(option_labels, selectable): - if label == selected_label: - selected_row = row - break - if selected_row and st.button( - "保存所选权重为默认配置", - key="save_decision_env_weights_batch", - ): - try: - app_cfg.agent_weights.update_from_dict(selected_row.get("权重", {})) - save_config(app_cfg) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("批量保存权重失败", extra={**LOG_EXTRA, "error": str(exc)}) - st.error(f"写入配置失败：{exc}") - else: - st.success( - f"已将序号 {selected_row['序号']} 的权重写入 config.json" - ) - else: - st.caption("暂无成功的结果可供保存。") - else: - st.caption("批量调参在最近一次执行中未产生结果。") - if st.button("清除批量调参结果", key="clear_decision_env_batch"): - st.session_state.pop(_DECISION_ENV_BATCH_RESULTS_KEY, None) - st.session_state.pop("decision_env_batch_select", None) - st.success("已清除批量调参结果缓存。")